Skip to main content
SpringerLink
Log in

A Survey of Controller Designs for New Generation UAVs: The Challenge of Uncertain Aerodynamic Parameters

  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper presents a survey of controller design techniques aimed at autonomous navigation and control of Unmanned Aerial Vehicles (UAVs), focusing on the challenge of aerodynamic uncertainty. Although many roadblocks exist, the most significant and challenging task for UAV navigation and control is the one of aerodynamic/model uncertainty. Current autopilots and controller designs for autonomous airplanes are mainly concerned with the feature of constant, unknown aerodynamic parameters, i.e., control and stability derivatives of the platform. This research focuses on a thorough investigation of the related theory and its applicability, centering on specific techniques that are able to control UAVs with rapidly changing, time-varying aerodynamic characteristics during flight. The scientific merit of this work is the comprehensive overview provided and the technical study that is performed, highlighting the advantages and disadvantages for each technique with respect to its efficiency and performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. V. Klein and E. A. Morelli, Aircraft System Identification: Theory and Practice, American Institute of Aeronautics and Astronautics, 2006.

  2. R. W. Beard and T. W. McLain, Small Unmanned Aircraft: Theory and Practice, Princeton University Press, 2012.

  3. B. L. Stevens, F. L. Lewis, and E. N. Johnson, Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems, John Wiley & Sons, 2015.

  4. M. B. Tischler and R. K. Remple, Aircraft and Rotorcraft System Identification, AIAA Education Series, 2006.

  5. K. Kanistras, M. J. Rutherford, and K. P. Valavanis, Foundations of Circulation Control Based Small-Scale Unmanned Aircraft, Springer, 2018.

  6. M. G. Michailidis, M. Agha, K. Kanistras, M. J. Rutherford, and K. P. Valavanis, “A controller design framework for a NextGen circulation control based UAV,” Proc. of IEEE Conference on Control Technology and Applications (CCTA), pp. 1542–1549, 2017.

  7. M. G. Michailidis, K. Kanistras, M. Agha, M. J. Rutherford, and K. P. Valavanis, “Robust nonlinear control of the longitudinal flight dynamics of a circulation control fixed wing UAV,” Proc. of IEEE Conference on Decision and Control (CDC), pp. 3920–3927, 2017.

  8. A. R. Rodriguez, “Morphing aircraft technology survey,” Proc. of 45th AIAA Aerospace Sciences Meeting and Exhibit, 2007.

  9. S. Barbarino, O. Bilgen, R. M. Ajaj, M. I. Friswell, and D. J. Inman, “A review of morphing aircraft,” Journal of Intelligent Material Systems and Structures, vol. 22, no. 9, pp. 823–877, 2011.

    Google Scholar 

  10. S. M. O. Tavares, S. J. Moreira, P. M. S. T. de Castro, and P. V. Gamboa, “Morphing aeronautical structures: a review focused on UAVs and durability assessment,” Proc. of IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), pp. 49–52, 2017.

  11. J. S. Flanagan, R. C. Strutzenberg, R. B. Myers, and J. E. Rodrian, “Development and flight testing of a morphing aircraft, the NextGen MFX-1,” Proc. of AIAA/ASME/ASCE/AHS/ASC Structures, Structual Dynamics, and Materials Conference, April 2007.

  12. A. Y. N. Sofla, S. A. Meguid, K. T. Tan, and W. K. Yeo, “Shape morphing of aircraft wing: status and challenges,” Materials & Design, vol. 31, no. 3, pp. 1284–1292, 2010.

    Google Scholar 

  13. J. D. Anderson Jr, Fundamentals of Aerodynamics, McGraw-Hill Education, 2010.

  14. M. Abdulrahim, “Flight dynamics and control of an aircraft with segmented control surfaces,” Proc. of 42nd AIAA Aerospace Sciences Meeting and Exhibit, 2003.

  15. J. Mo and A. Z. Chen, “UAV delivery system design and analysis,” Proc. of 17th Australian International Aerospace Congress (AIAC), 2017.

  16. M. Erdelj, E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, “Help from the sky: leveraging UAVs for disaster management,” IEEE Pervasive Computing, vol. 16, no. 1, pp. 24–32, 2017.

    Google Scholar 

  17. A. Ollero and L. Merino, “Control and perception techniques for aerial robotics,” Annual reviews in Control, vol. 28, no. 2, pp. 167–178, 2004.

    Google Scholar 

  18. A. Puri, A Survey of Unmanned Aerial Vehicles (UAV) for Traffic Surveillance, Department of Computer Science and Engineering, University of South Florida, 2005.

  19. H. Chen, X. M. Wang, and Y. Li, “A survey of autonomous control for UAV,” Proc. of IEEE International Conference on Artificial Intelligence and Computational Intelligence (AICI), pp. 267–271, 2009.

  20. B. M. Albaker and N. A. Rahim, “A survey of collision avoidance approaches for unmanned aerial vehicles,” Proc. of International Conference for Technical Postgraduates (TECHPOS), pp. 1–7, 2009.

  21. C. Goerzen, Z. Kong, and B. Mettler, “A survey of motion planning algorithms from the perspective of autonomous UAV guidance,” Journal of Intelligent and Robotic Systems, vol. 57, no. 1–4, p. 65, 2010.

    MATH  Google Scholar 

  22. H. Chao, Y. Cao, and Y. Chen, “Autopilots for small unmanned aerial vehicles: a survey,” International Journal of Control, Automation and Systems, vol. 8, no. 1, pp. 36–44, 2010.

    Google Scholar 

  23. S. M. Adams and C. J. Friedland, “A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management,” International Workshop on Remote Sensing for Disaster Response, 2011.

  24. N. Dadkhah and B. Mettler, “Survey of motion planning literature in the presence of uncertainty: Considerations for UAV guidance,” Journal of Intelligent & Robotic Systems, vol. 65, no. 1–4, pp. 233–246, 2012.

    Google Scholar 

  25. G. Cai, J. Dias, and L. Seneviratne, “A survey of small-scale unmanned aerial vehicles: recent advances and future development trends,” Unmanned Systems, vol. 2, no. 2, pp. 175–199, 2014.

    Google Scholar 

  26. L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UAV communication networks,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1123–1152, 2016.

    Google Scholar 

  27. Y. Lu, Z. Xue, G. S. Xia, and L. Zhang, “A survey on vision-based UAV navigation,” Geo-spatial Information Science, vol. 21, no. 1, pp. 21–32, 2018.

    Google Scholar 

  28. T. M. Adami and J. J. Zhu, “6DOF flight control of fixed-wing aircraft by trajectory linearization,” Proc. of IEEE American Control Conference (ACC), pp. 1610–1617, 2011.

  29. J. Alvarenga, N. I. Vitzilaios, K. P. Valavanis, and M. J. Rutherford, “Survey of unmanned helicopter model-based navigation and control techniques,” Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 87–138, 2015.

    Google Scholar 

  30. P. Jetley, P. B. Sujit, and S. Saripalli, “Safe landing of fixed wing UAVs,” Proc. of IEEE International Conference on Dependable Systems and Networks Workshop (DSN-W), pp. 2–9, 2017.

  31. A. K. Pandey, T. Chaudhary, S. Mishra, and S. Verma, “Longitudinal control of small unmanned aerial vehicle by PID controller,” Intelligent Communication, Control and Devices, Springer, pp. 923–931, 2018.

  32. P. Poksawat, L. Wang, and A. Mohamed, “Gain scheduled attitude control of fixed-wing UAV with automatic controller tuning,” IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1192–1203, July 2018.

    Google Scholar 

  33. T. Espinoza-Fraire, A. Dzul, F. Cortes-Martinez, and W. Giernacki, “Real-time implementation and flight tests using linear and nonlinear controllers for a fixed-wing miniature aerial vehicle (MAV),” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 392–396, 2018.

    Google Scholar 

  34. A. Sarhan and S. Qin, “Robust adaptive flight controller for UAV systems,” Proc. of IEEE International Conference on Information Science and Control Engineering (ICISCE), pp. 1214–1219, 2017.

  35. A. M. Lyapunov, “The general problem of the stability of motion,” International Journal of Control, vol. 55, no. 3, pp. 531–534, 1992.

    MathSciNet  Google Scholar 

  36. M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic, Nonlinear and Adaptive Control Design, Wiley, 1995.

  37. O. Harkegard, Backstepping and Control Allocation with Applications to Flight Control, Ph.D. thesis, Linkopings universitet, 2003.

  38. W. Ren and E. Atkins, “Nonlinear trajectory tracking for fixed wing UAVs via backstepping and parameter adaptation,” Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005.

  39. A. Brezoescu, T. Espinoza, P. Castillo, and R. Lozano, “Adaptive trajectory following for a fixed-wing UAV in presence of crosswind,” Journal of Intelligent & Robotic Systems, vol. 69, no. 1–4, pp. 257–271, 2013.

    Google Scholar 

  40. S. Yoon, Y. Kim, and S. Park, “Constrained adaptive back-stepping controller design for aircraft landing in wind disturbance and actuator stuck,” International Journal of Aeronautical and Space Sciences, vol. 13, no. 1, pp. 74–89, 2012.

    Google Scholar 

  41. Y. Han, P. Li, and Z. Zheng, “A non-decoupled backstepping control for fixed-wing UAVs with multivariable fixed-time sliding mode disturbance observer,” Transactions of the Institute of Measurement and Control, vol. 41, no. 4, 963–974, 2019.

    Google Scholar 

  42. Y. C. Wang, W. S. Chen, S. X. Zhang, J. W. Zhu, and L. J. Cao, “Command-filtered incremental backstepping controller for small unmanned aerial vehicles,” Journal of Guidance, Control, and Dynamics, vol. 41, no. 4, pp. 954–967, 2018.

    Google Scholar 

  43. M. N. R. B. M. Afandi, M. B. Hassan, G. Suhardi, Y. Zhou, and L. Danny, “Comparison of backstepping, fuzzy-PID, and PID control techniques using X8 model in relation to A* path planning,” Proc. of IEEE International Conference on Intelligent Transportation Engineering (ICITE), pp. 340–345, 2017.

  44. V. Utkin, “Sliding mode control,” Control, Systems, Robotics and Automation: Nonlinear, Distributed, and Time Delay Systems, 2009.

  45. S. Vaidyanathan and C. H. Lien, Applications of Sliding Mode Control in Science and Engineering, Springer, 2017.

  46. H. K. Khalil, Nonlinear Control, Prentice Hall, 2014.

  47. H. Castaneda, O. S. Salas-Pena, and J. de Leon-Morales, “Extended observer based on adaptive second order sliding mode control for a fixed wing UAV,” ISA Transactions, vol. 66, pp. 226–232, 2017.

    Google Scholar 

  48. U. Gunes, A. Sel, C. Kasnakoglu, and U. Kaynak, “Output feedback sliding mode control of a fixed-wing UAV under rudder loss,” AIAA Scitech 2019 Forum, 2019.

  49. Z. Zheng, Z. Jin, L. Sun, and M. Zhu, “Adaptive sliding mode relative motion control for autonomous carrier landing of fixed-wing unmanned aerial vehicles”. IEEE Access, vol. 5, pp. 5556–5565, 2017.

    Google Scholar 

  50. A. T. Espinoza-Fraire, Y. Chen, A. Dzul, R. Lozano, and R. Juarez, “Fixed-wing MAV adaptive PD control based on a modified MIT rule with sliding-mode control,” Journal of Intelligent & Robotic Systems, vol. 91, no. 1, pp. 101–114, July 2018.

    Google Scholar 

  51. B. Lu, Y. Fang, and N. Sun, “Continuous sliding mode control strategy for a class of nonlinear underactuated systems,” IEEE Transactions on Automatic Control, vol. 63, no. 10, pp. 3471–3478, October 2018.

    MathSciNet  MATH  Google Scholar 

  52. G. Perozzi, D. Efimov, J. M. Biannic, L. Planckaert and P. Coton, “Wind rejection via quasi-continuous sliding mode technique to control safely a mini drone,” Proc. of European Conference for Aeronautics and Space Science, 2017.

  53. H. Rios, J. Gonzalez-Sierra, and A. Dzul, “Robust tracking output-control for a quad-rotor: a continuous sliding-mode approach,” Journal of the Franklin Institute, vol. 354, no. 15, pp. 6672–6691, 2017.

    MathSciNet  MATH  Google Scholar 

  54. A. J. Munoz-Vazquez, V. Parra-Vega, and A. Sanchez-Orta, “Continuous fractional-order sliding PI control for nonlinear systems subject to non-differentiable disturbances,” Asian Journal of Control, vol. 19, no. 1, pp. 279–288, 2017.

    MathSciNet  MATH  Google Scholar 

  55. T. Espinoza, A. E. Dzul, R. Lozano, and P. Parada, “Backstepping-sliding mode controllers applied to a fixed-wing UAV,” Journal of Intelligent & Robotic Systems, vol. 73, no. 1–4, pp. 67–79, 2014.

    Google Scholar 

  56. P. Ru and K. Subbarao, “Nonlinear model predictive control for unmanned aerial vehicles,” Aerospace, vol. 4, no. 2, 2017.

    Google Scholar 

  57. H. Ulker, C. Baykara, and C. Ozsoy, “Design of MPCs for a fixed wing UAV,” Aircraft Engineering and Aerospace Technology, vol. 89, no. 6, pp. 893–901, 2017.

    Google Scholar 

  58. U. Eren, A. Prach, B. B. Kocer, S. V. Rakovic, E. Kayacan, and B. Acikmese, “Model predictive control in aerospace systems: Current state and opportunities,” Journal of Guidance, Control, and Dynamics, vol. 40, no. 7, 2017.

    Google Scholar 

  59. Y. Kang and J. K. Hedrick, “Linear tracking for a fixed-wing UAV using nonlinear model predictive control,” IEEE Transactions on Control Systems Technology, vol. 17, no. 5, pp. 1202–1210, 2009.

    Google Scholar 

  60. K. Yang, Y. Kang, and S. Sukkarieh, “Adaptive nonlinear model predictive path-following control for a fixed-wing unmanned aerial vehicle,” International Journal of Control, Automation and Systems, vol. 11, no. 1, pp. 65–74, 2013.

    Google Scholar 

  61. T. J. Stastny, A. Dash, and R. Siegwart, “Nonlinear mpc for fixed-wing uav trajectory tracking: implementation and flight experiments,” Proc. of AIAA Guidance, Navigation, and Control Conference, 2017.

  62. T. Stastny and R. Siegwart, “Nonlinear model predictive guidance for fixed-wing uavs using identified control augmented dynamics,” Proc. of IEEE International Conference on Unmanned Aircraft Systems (ICUAS), pp. 432–442, 2018.

  63. F. Gavilan, R. Vazquez, A. Lobato, M. de la Rosa, A. Gallego, E. F. Camacho, M. W. Hardt, and F. A. Navarro, “Increasing predictability and performance in UAS flight contingencies using AIDL and MPC,” Proc. of AIAA Guidance, Navigation, and Control Conference, 2018.

  64. R. P. K. Jain, A. P. Aguiar, A. Alessandretti, and J. Borges de Sousa, “Moving path following control of constrained underactuated vehicles: a nonlinear model predictive control approach,” Proc. of AIAA Information Systems — AIAA Infotech Aerospace, 2018.

  65. K. J. Astrom and B. Wittenmark, Adaptive Control, Courier Corporation, 2013.

  66. R. E. Bellman, Adaptive Control Processes: A Guided Tour, Princeton University Press, 2015.

  67. F. Gavilan, J. A. Acosta, and R. Vazquez, “Control of the longitudinal flight dynamics of an UAV using adaptive backstepping,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 1892–1897, 2011.

    Google Scholar 

  68. P. R. Ambati and R. Padhi, “Robust auto-landing of fixed-wing UAVs using neuro-adaptive design,” Control Engineering Practice, vol. 60, pp. 218–232, 2017.

    Google Scholar 

  69. H. A. de Oliveira and P. F. F. Rosa, “Adaptive genetic neuro-fuzzy attitude control for a fixed wing UAV,” Proc. of IEEE International Conference on Industrial Technology (ICIT), pp. 726–731, 2017.

  70. D. Noble and S. Bhandari, “Neural network based nonlinear model reference adaptive controller for an unmanned aerial vehicle,” Proc. of IEEE International Conference on Unmanned Aircraft Systems (ICUAS), pp. 94–103, 2017.

  71. H. Z. I. Khan, J. Rajput, S. Ahmed, and J. Riaz, “An adaptive flare scheme for autonomous landing of a fixed-wing UAV,” Proc. of IEEE International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 425–430, 2019.

  72. P. A. Ioannou and J. Sun, Robust Adaptive Control, Prentice-Hall Upper Saddle River, 2010 (Reprint).

  73. J. P. Hespanha, D. Liberzon, and A. S. Morse, “Overcoming the limitations of adaptive control by means of logic-based switching,” Systems & Control Letters, vol. 49, no. 1, pp. 49–65, 2003.

    MathSciNet  MATH  Google Scholar 

  74. M. Stefanovic and M. G. Safonov, Safe Adaptive Control: Data-driven Stability Analysis and Robust Synthesis, Springer, 2011.

  75. L. Long and J. Zhao, “Adaptive control for a class of highorder switched nonlinearly parameterized systems,” International Journal of Robust and Nonlinear Control, vol. 27, no. 4, pp. 547–565, 2017.

    MathSciNet  MATH  Google Scholar 

  76. H. Wang, Z. Wang, Y. J. Liu, and S. Tong, “Fuzzy tracking adaptive control of discrete-time switched nonlinear systems,” Fuzzy Sets and Systems, vol. 316, pp. 35–48, 2017.

    MathSciNet  MATH  Google Scholar 

  77. Y. Li, S. Sui, and S. Tong, “Adaptive fuzzy control design for stochastic nonlinear switched systems with arbitrary switchings and unmodeled dynamics,” IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 403–414, 2017.

    Google Scholar 

  78. Y. Li and S. Tong, “Adaptive fuzzy output-feedback stabilization control for a class of switched nonstrict-feedback nonlinear systems,” IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 1007–1016, 2017.

    Google Scholar 

  79. S. J. Cho, J. S. Lee, J. Kim, T. Y. Kuc, P. H. Chang, and M. Jin, “Adaptive time-delay control with a supervising switching technique for robot manipulators,” Transactions of the Institute of Measurement and Control, vol. 39, no. 9, pp. 1374–1382, 2017.

    Google Scholar 

  80. D. Enns, D. Bugajski, R. Hendrick, and G. Stein, “Dynamic inversion: an evolving methodology for flight control design,” International Journal of Control, vol. 59, no. 1, pp. 71–91, 1994.

    MATH  Google Scholar 

  81. Y. Kawakami and K. Uchiyama, “Nonlinear controller design for transition flight of a fixed-wing UAV with input constraints,” Proc. of AIAA Guidance, Navigation, and Control Conference, 2017.

  82. H. Chang, Y. Liu, Y. Wang, and X. Zheng, “A modified nonlinear dynamic inversion method for attitude control of UAVs under persistent disturbances,” Proc. of IEEE International Conference on Information and Automation (ICIA), pp. 715–721, 2017.

  83. E. J. Smeur, G. C. H. E. de Croon, and Q. Chu, “Cascaded incremental nonlinear dynamic inversion for MAV disturbance rejection,” Control Engineering Practice, vol. 73, pp. 79–90, 2018.

    Google Scholar 

  84. A. H. Amlashi, R. M. Gharamaleki, M. H. H. Nejad, and M. Mirzaei, “Design of estimator-based nonlinear dynamic inversion controller and nonlinear regulator for robust trajectory tracking with aerial vehicles,” International Journal of Dynamics and Control, vol. 6, no. 2, pp. 707–725, 2018.

    MathSciNet  Google Scholar 

  85. E. Safwat, W. Zhang, M. Wu, Y. Lyu, and Q. Jia, “Robust path following controller for unmanned aerial vehicle based on carrot chasing guidance law using dynamic inversion” Proc. of IEEE International Conference on Control, Automation and Systems (ICCAS), pp. 1444–1450, 2018.

  86. N. B. Silva, J. V. Fontes, R. S. Inoue, and K. R. Branco, “Dynamic inversion and gain-scheduling control for an autonomous aerial vehicle with multiple flight stages,” Journal of Control, Automation and Electrical Systems, vol. 29, no. 3, pp. 328–339, 2018.

    Google Scholar 

  87. S. Kurnaz, O. Cetin, and O. Kaynak, “Fuzzy logic based approach to design of flight control and navigation tasks for autonomous unmanned aerial vehicles,” Journal of Intelligent and Robotic Systems, vol. 54, no. 1–3, pp. 229–244, 2009.

    Google Scholar 

  88. T. Espinoza, A. Dzul, and M. Llama, “Linear and nonlinear controllers applied to fixed-wing UAV,” International Journal of Advanced Robotic Systems, vol. 10, no. 1, 2013.

    Google Scholar 

  89. T. Lee and Y. Kim, “Nonlinear adaptive flight control using backstepping and neural networks controller,” Journal of Guidance, Control, and Dynamics, vol. 24, no. 4, pp. 675–682, 2001.

    Google Scholar 

  90. E. Kayacan, M. A. Khanesar, J. Rubio-Hervas, and M. Reyhanoglu, “Learning control of fixed-wing unmanned aerial vehicles using fuzzy neural networks,” International Journal of Aerospace Engineering, vol. 2017, Article ID 5402809, 2017.

  91. H. A. de Oliveira and P. F. F. Rosa, “Genetic neuro-fuzzy approach for unmanned fixed wing attitude control,” Proc. of IEEE International Conference on Military Technologies (ICMT), pp. 485–492, 2017.

  92. F. Santoso, M. A. Garratt, and S. G. Anavatti, “State-of-the-art intelligent flight control systems in unmanned aerial vehicles,” IEEE Transactions on Automation Science and Engineering, vol. 15, no. 2, pp. 613–627, 2018.

    Google Scholar 

  93. B. Kiumarsi, K. G. Vamvoudakis, H. Modares, and F. L. Lewis, “Optimal and autonomous control using reinforcement learning: a survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2042–2062, 2018.

    MathSciNet  Google Scholar 

  94. W. He, T. Meng, X. He, and S. S. Ge, “Unified iterative learning control for flexible structures with input constraints,” Automatica, vol. 96, pp. 326–336, 2018.

    MathSciNet  MATH  Google Scholar 

  95. P. Mars, Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications, CRC press, 2018.

  96. T. Meng and W. He, “Iterative learning control of a robotic arm experiment platform with input constraint,” IEEE Transactions on Industrial Electronics, vol. 65, no. 1, pp. 664–672, 2018.

    Google Scholar 

  97. K. Choromanski, V. Sindhwani, B. Jones, D. Jourdan, M. Chociej, and B. Boots, “Learning-based air data system for safe and efficient control of fixed-wing aerial vehicles,” Proc. of IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2018.

  98. W. Koch, R. Mancuso, R. West, and A. Bestavros, “Reinforcement learning for UAV attitude control,” ACM Transactions on Cyber-Physical Systems, vol. 3, no. 2, Article no. 22, 2019.

    Google Scholar 

  99. W. J. Rugh and J. S. Shamma, “Research on gain scheduling,” Automatica, vol. 36, no. 10, pp. 1401–1425, 2000.

    MathSciNet  MATH  Google Scholar 

  100. A. Marcos and G. J. Balas, “Development of linear-parameter-varying models for aircraft,” Journal of Guidance Control and Dynamics, vol. 27, no. 2, pp. 218–228, 2004.

    Google Scholar 

  101. C. V. Girish, F. Emilio, H. P. Jonathan, and L. Hugh, “Nonlinear flight control techniques for unmanned aerial vehicles,” Handbook of Unmanned Aerial Vehicles, pp. 577–612, Springer, 2015.

  102. P. Shao, Z. Zhou, S. Ma, and L. Bin, “Structural robust gain-scheduled PID control and application on a morphing wing UAV,” IEEE Chinese Control Conference (CCC), pp. 3236–3241, 2017.

  103. T. Yue, L. Wang, and J. Ai, “Longitudinal integrated linear parameter varying control for morphing aircraft in large flight envelope,” Proc. of AIAA Atmospheric Flight Mechanics Conference, 2017.

  104. J. Stephan and W. Fichter, “Gain-scheduled multivariable flight control under uncertain trim conditions,” Proc. of AIAA Guidance, Navigation, and Control Conference, 2018.

  105. I. R. Petersen, V. A. Ugrinovskii, and A. V. Savkin, Robust Control Design Using H∞Methods, Springer Science & Business Media, 2012.

  106. K. Glover, H-Infinity Control, Encyclopedia of Systems and Control, 2015.

  107. H. C. Ferreira, R. S. Baptista, J. Y. Ishihara, and G. A. Borges, “Disturbance rejection in a fixed wing UAV using nonlinear H∞ state feedback,” Proc. of IEEE International Conference on Control and Automation (ICCA), pp. 386–391, 2011.

  108. J. Lesprier, J. M. Biannic, and C. Roos, “Modeling and robust nonlinear control of a fixed-wing UAV,” Proc. of IEEE Conference on Control Applications (CCA), pp. 1334–1339, 2015.

  109. J. M. Biannic, C. Roos, and J. Lesprier, “Nonlinear structured H∞ controllers for parameter-dependent uncertain systems with application to aircraft landing,” Proc. of IEEE Conference on Control Applications (CCA), 2014.

  110. C. C. Kung, “Nonlinear H∞ robust control applied to F-16 aircraft with mass uncertainty using control surface inverse algorithm,” Journal of the Franklin Institute, vol. 345, no. 8, pp. 851–876, 2008.

    MathSciNet  MATH  Google Scholar 

  111. G. A. Garcia, S. Kashmiri, and D. Shukla, “Nonlinear control based on H-infinity theory for autonomous aerial vehicle,” Proc. of IEEE International Conference on Unmanned Aircraft Systems (ICUAS), pp. 336–345, 2017.

  112. G. J. Balas, J. C. Doyle, K. Glover, A. Packard, and R. Smith, “µ-analysis and synthesis toolbox,” MUSYN Inc. and The MathWorks, 1993.

  113. G. Balas, R. Chiang, A. Packard, and M. Safonov, “Robust control toolbox, For Use with Matlab,” User’s Guide, 2005.

  114. M. G. Michailidis, K. Kanistras, M. Agha, M. J. Rutherford, and K. P. Valavanis, “Nonlinear control of fixed-wing UAVs with time-varying aerodynamic uncertainties via µ-synthesis,” Proc. of IEEE Conference on Decision and Control (CDC), pp. 6314–6321, 2018.

  115. G. Yin, N. Chen, and P. Li, “Improving handling stability performance of four-wheel steering vehicle via µ-synthsis robust control,” IEEE Transactions on Vehicular Technology, vol. 56, no. 5, pp. 2432–2439, 2007.

    Google Scholar 

  116. L. Qiu, G. Fan, J. Yi, and W. Yu, “Robust hybrid Controller design based on feedback linearization and µ-synthesis for UAV,” Proc. of IEEE International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 858–861, 2009.

Download references

Author information

Authors and Affiliations

  1. University of Denver Unmanned Systems Research Institute (DU2SRI), 2155 East Wesley Avenue, Denver, CO, 80208, USA

    Michail G. Michailidis, Matthew J. Rutherford & Kimon P. Valavanis

Authors
  1. Michail G. Michailidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew J. Rutherford
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimon P. Valavanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michail G. Michailidis.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor H. Jin Kim under the direction of Editor Chan Gook Park. This research was supported in part by National Science Foundation (NSF) Grant CMMI/DCSD 1728454.

Michail G. Michailidis received his B.Sc. in Mathematics and M.Sc. in Applied Mathematics from the Aristotle University of Thessaloniki, Greece. He graduated from the University of Denver in 2019 with a Ph.D. in Electrical Engineering and he is currently a research scientist at DU2SRI. His ongoing research project is controller design for unmanned aerial vehicles with time-varying aerodynamic uncertainties and his area of expertise is modeling, control and performance optimization of dynamical systems.

Matthew J. Rutherford is an Associate Professor in the Department of Computer Science with a joint appointment in the Department of Electrical and Computer Engineering at the University of Denver. He is also a Deputy Director of the DU Unmanned Systems Research Institute (DU2SRI). His research portfolio includes: the development of advanced controls and communication mechanisms for autonomous aerial and ground robots; applications of real-time computer vision to robotics problems using GPU-based parallel processing; testing and dynamic evaluation of embedded, real-time systems; development of complex mechatronic systems (mechanical, electrical, and software); the development of software techniques to reduce the amount of energy being consumed by hardware; development of a high-precision propulsion system for underwater robots.

Kimon P. Valavanis is a John Evans Professor and Director of Research and Innovation in the Department of Electrical and Computer Engineering, with a joint appointment in the Department of Computer Science at the University of Denver. He is also Director of the DU Unmanned Systems Research Institute (DU2SRI). He holds a Guest Professor appointment in the Department of Telecommunications, Faculty of Electrical Engineering and Computing at the University of Zagreb, Croatia. His research interests span the areas of intelligent control, robotics and automation, and distributed intelligent systems, focusing on: integrated control and diagnostics of unmanned systems; modeling and formation control of cooperative robot teams; navigation/control of unmanned aerial vehicles; modeling, design and development of complex mechatronic systems; design of the next generation of unmanned systems; mathematical theories for intelligent machines. He is Fellow of the American Association for the Advancement of Science (AAAS), Senior Member of IEEE, Editor-in-Chief of the Journal of Intelligent and Robotic Systems (Springer), and Fulbright Scholar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Michailidis, M.G., Rutherford, M.J. & Valavanis, K.P. A Survey of Controller Designs for New Generation UAVs: The Challenge of Uncertain Aerodynamic Parameters. Int. J. Control Autom. Syst. 18, 801–816 (2020). https://doi.org/10.1007/s12555-018-0489-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-018-0489-8

Keywords

Search

Navigation